Shark: Fast Data Analysis Using Coarse-grained Distributed Memory

Summary: Shark is a data-analysis system built on a coarse-grained distributed shared-memory abstraction, unifying SQL querying with near-data analytics. Scales to thousands of fault-tolerant nodes; delivers 40x faster queries vs Hive and 25x faster ML vs MapReduce on large datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 4584
Venue: SIGMOD
Year: 2012
Pagerank: 8.6609871e-05
Overall Rank: 2,487 | 82.72%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,097	Trill: A High-Performance Incremental Query Processor for Diverse Analytics	2015	VLDB	0.00014083973
1,151	Blink and It's Done: Interactive Queries on Very Large Data	2012	VLDB	0.00013634671
1,488	Scuba: Diving into Data at Facebook	2013	VLDB	0.00011690191
1,588	Druid: A Real-time Analytical Data Store	2014	SIGMOD	0.00011232949
2,198	DITA: Distributed In-Memory Trajectory Analytics	2018	SIGMOD	9.3096071e-05
2,494	DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models	2019	SIGMOD	8.6457436e-05
4,226	Hyper Dimension Shuffle: Efficient Data Repartition at Petabyte Scale in SCOPE	2019	VLDB	6.3382156e-05
7,195	BSMA: A Benchmark for Analytical Queries over Social Media Data	2014	VLDB	4.799032e-05
7,748	TSCache: An Efficient Flash-based Caching Scheme for Time-series Data Workloads	2021	VLDB	4.6571687e-05
9,893	OceanRT: Real-Time Analytics over Large Temporal Data	2014	SIGMOD	4.2561802e-05
12,013	Design and Implementation of a Real-Time Interactive Analytics System for Large Spatio-Temporal Data	2014	VLDB	4.1905499e-05
12,036	D-Hive: Data Bees Pollinating RDF, Text, and Time	2013	CIDR	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
42	A Comparison of Approaches to Large-Scale Data Analysis	2009	SIGMOD	0.00073570328

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